


'''simple docstring'''


import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_						:  int				  =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_						:  Dict				  =						[]
for i in range(6):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
				)
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
				)
				rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
				rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
				rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
				rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
				)
				rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
				rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
				rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
				# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
				)
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
				)
				rename_keys.append(
				    (
				        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
				        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
				    )
				)
				rename_keys.append(
				    (
				        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
				        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
				    )
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
				rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
				rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
				rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
				)
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
				)
				rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
				rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
        ('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= state_dict.pop(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= val







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_SCREAMING_SNAKE_CASE					:			str							= key.replace("""backbone.0.body"""				, """backbone.conv_encoder.model"""	)
												_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= value
								else:
												_SCREAMING_SNAKE_CASE					:			int							= value

				return new_state_dict







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Any							= """"""

				# first: transformer encoder
				for i in range(6	):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								_SCREAMING_SNAKE_CASE					:			int							= state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
								_SCREAMING_SNAKE_CASE					:			Tuple							= state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""	)
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE					:			Any							= in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE					:			Dict							= in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE					:			Tuple							= in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE					:			str							= in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= in_proj_bias[-256:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6	):
								# read in weights + bias of input projection layer of self-attention
								_SCREAMING_SNAKE_CASE					:			Dict							= state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
								_SCREAMING_SNAKE_CASE					:			Tuple							= state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""	)
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE					:			Any							= in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE					:			Dict							= in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								_SCREAMING_SNAKE_CASE					:			List[Any]							= state_dict.pop(
								    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
								_SCREAMING_SNAKE_CASE					:			Dict							= state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""	)
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								_SCREAMING_SNAKE_CASE					:			Dict							= in_proj_weight_cross_attn[:256, :]
								_SCREAMING_SNAKE_CASE					:			int							= in_proj_bias_cross_attn[:256]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= in_proj_weight_cross_attn[256:512, :]
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= in_proj_bias_cross_attn[256:512]
								_SCREAMING_SNAKE_CASE					:			int							= in_proj_weight_cross_attn[-256:, :]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= in_proj_bias_cross_attn[-256:]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= image.size
				_SCREAMING_SNAKE_CASE					:			int							= max(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= 800 if """detection""" in checkpoint_url else 1000
				_SCREAMING_SNAKE_CASE					:			Dict							= target_max_size / current_max_size
				_SCREAMING_SNAKE_CASE					:			Any							= image.resize((int(round(scale * width	)	), int(round(scale * height	)	))	)

				return resized_image







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			str							= F.to_tensor(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			int							= F.normalize(SCREAMING_SNAKE_CASE__				, mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]				, std=[0.2_2_9, 0.2_2_4, 0.2_2_5]	)
				return image







@torch.no_grad()
def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				logger.info("""Converting model..."""	)

				# load original state dict
				_SCREAMING_SNAKE_CASE					:			int							= torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__				, map_location="""cpu"""	)
				# rename keys
				for src, dest in rename_keys:
								rename_key(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= rename_backbone_keys(SCREAMING_SNAKE_CASE__	)
				# query, key and value matrices need special treatment
				read_in_q_k_v(SCREAMING_SNAKE_CASE__	)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= """model."""
				for key in state_dict.copy().keys():
								if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= state_dict.pop(SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			List[Any]							= val
    # create HuggingFace model and load state dict
				_SCREAMING_SNAKE_CASE					:			List[str]							= TableTransformerConfig(
				    backbone="""resnet18"""				, mask_loss_coefficient=1				, dice_loss_coefficient=1				, ce_loss_coefficient=1				, bbox_loss_coefficient=5				, giou_loss_coefficient=2				, eos_coefficient=0.4				, class_cost=1				, bbox_cost=5				, giou_cost=2				, )

				if "detection" in checkpoint_url:
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 15
								_SCREAMING_SNAKE_CASE					:			int							= 2
								_SCREAMING_SNAKE_CASE					:			List[Any]							= {0: """table""", 1: """table rotated"""}
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= idalabel
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= {v: k for k, v in idalabel.items()}
				else:
								_SCREAMING_SNAKE_CASE					:			Dict							= 125
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 6
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= {
								    0: """table""",
								    1: """table column""",
								    2: """table row""",
								    3: """table column header""",
								    4: """table projected row header""",
								    5: """table spanning cell""",
								}
								_SCREAMING_SNAKE_CASE					:			List[Any]							= idalabel
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= {v: k for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE					:			Tuple							= DetrImageProcessor(
				    format="""coco_detection"""				, max_size=800 if """detection""" in checkpoint_url else 1000	)
				_SCREAMING_SNAKE_CASE					:			str							= TableTransformerForObjectDetection(SCREAMING_SNAKE_CASE__	)
				model.load_state_dict(SCREAMING_SNAKE_CASE__	)
				model.eval()

				# verify our conversion
				_SCREAMING_SNAKE_CASE					:			int							= """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hf_hub_download(repo_id="""nielsr/example-pdf"""				, repo_type="""dataset"""				, filename=SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			int							= Image.open(SCREAMING_SNAKE_CASE__	).convert("""RGB"""	)
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= normalize(resize(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)	).unsqueeze(0	)

				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= model(SCREAMING_SNAKE_CASE__	)

				if "detection" in checkpoint_url:
								_SCREAMING_SNAKE_CASE					:			Dict							= (1, 15, 3)
								_SCREAMING_SNAKE_CASE					:			Tuple							= torch.tensor(
								    [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]	)

				else:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= (1, 125, 7)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= torch.tensor(
								    [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]	)
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]	)

				assert outputs.logits.shape == expected_shape
				assert torch.allclose(outputs.logits[0, :3, :3]				, SCREAMING_SNAKE_CASE__				, atol=1E-4	)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]				, SCREAMING_SNAKE_CASE__				, atol=1E-4	)
				print("""Looks ok!"""	)

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""	)
								Path(SCREAMING_SNAKE_CASE__	).mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)
								model.save_pretrained(SCREAMING_SNAKE_CASE__	)
								image_processor.save_pretrained(SCREAMING_SNAKE_CASE__	)

				if push_to_hub:
								# Push model to HF hub
								logger.info("""Pushing model to the hub..."""	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= (
								    """microsoft/table-transformer-detection"""
								    if """detection""" in checkpoint_url
								    else """microsoft/table-transformer-structure-recognition"""
								)
								model.push_to_hub(SCREAMING_SNAKE_CASE__	)
								image_processor.push_to_hub(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						argparse.ArgumentParser()

				parser.add_argument(
				    '--checkpoint_url',
				    default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
				    type=str,
				    choices=[
				        'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
				        'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
				    ],
				    help='URL of the Table Transformer checkpoint you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)
				UpperCAmelCase_						:  List[str]				  =						parser.parse_args()
				convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''


import math







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				assert isinstance(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	) and (
				    number >= 0
				), "'number' must been an int and positive"

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or not number % 2:
								# Negatives, 0, 1 and all even numbers are not primes
								return False

				_SCREAMING_SNAKE_CASE					:			List[str]							= range(3				, int(math.sqrt(SCREAMING_SNAKE_CASE__	) + 1	)				, 2	)
				return not any(not number % i for i in odd_numbers	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=1				, **SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= factor * value
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= value

				while not is_prime(SCREAMING_SNAKE_CASE__	):
								value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

				if value == first_value_val:
								return next_prime(value + 1				, **SCREAMING_SNAKE_CASE__	)
				return value




